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Record W2605180792 · doi:10.24908/pceea.v0i0.6510

UPDATE ON THE DEVELOPMENT OF ANALYTIC RUBRICS FOR COMPETENCY ASSESSMENT (DARCA)

2017· article· en· W2605180792 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRubricTeamworkGrading (engineering)Computer scienceSet (abstract data type)Process (computing)Standards-based assessmentMathematics educationPsychologyEngineeringEducational assessmentPolitical science

Abstract

fetched live from OpenAlex

The shift towards outcomes-based assessment in higher education has necessitated the exploration and development of valid measurement tools. Given this trend, the current project seeks to develop a set of generic analytic rubrics for the purpose of assessing learning outcomes in the core competency areas of design, communication, teamwork, problem analysis and investigation. This paper will provide an update on the original paper presented at CEEA 2015, in which the approach to rubric development for communication, design and teamwork was discussed. The current paper will detail the process of testing the communication, design and teamwork rubrics. In particular, it will report on the progress achieved in shadow testing, where teaching assistants and/or course instructors with grading experience (“assessors”) are asked to evaluate samples of student work using selected rows from the rubrics. The results of shadow testing will be presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.332
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it